| Title: | Association and risk prediction of 19 complex diseases with polygenic scores and socioeconomic status |
| Journal: | Communications Medicine |
| Published: | 11 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42277436/ |
| DOI: | https://doi.org/10.1038/s43856-026-01716-1 |
| URL: | https://www.nature.com/articles/s43856-026-01716-1_reference.pdf |
Publication 20769
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Abstract
BackgroundDifferences in disease risk are linked to inherited genetic variation and social circumstances, but how these factors jointly relate to multiple diseases is not fully understood. This study investigates the independent and combined associations of genetic predisposition and socioeconomic status with common diseases.MethodsWe analyzed data from 743,194 adults from three biobank studies in Finland and the United Kingdom, including European and non-European ancestry individuals. Genetic predisposition was measured using polygenic scores for 19 common diseases. Socioeconomic status was assessed through education and occupation. We evaluated associations of genetic and socioeconomic factors with disease risk and whether genetic associations differed across education levels. Models including both genetic and educational factors were compared with models including only one factor and models including their interaction.ResultsHere, we show that both genetic predisposition and socioeconomic status are associated with disease risk across most conditions. For seven diseases, genetic associations are stronger in highly educated groups, while occupation shows smaller differences. Patterns are generally consistent across ancestry groups, though education associations are less stable in non-European individuals. While both education and polygenic scores independently improved prediction, combining both provided additional modest improvement to prediction for 16 and 12 out of 19 diseases in FinnGen and the UK Biobank, respectively.ConclusionsGenetic variation and social circumstances are linked to differences in disease risk both independently, and in combination with one another. Considering both factors modestly improves identification of higher-risk groups and highlights the value of accounting for social context alongside inherited variation in population-level assessments.</p>
12 Authors
- Fiona A. Hagenbeek
- Anne Richmond
- Max Tamlander
- Kira Detrois
- Zhiyu Yang
- Tuomo Hartonen
- Daniel L. McCartney
- Riccardo E. Marioni
- Pekka Martikainen
- Nina Mars
- Andrea Ganna
- Samuli Ripatti
1 Application
| Application ID | Title |
|---|---|
| 78537 | International consortium for integrative genomics prediction (INTERVENE) |
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